Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

714
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
714
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

699
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
699
The X̄ Chart00:58

The X̄ Chart

541
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
541
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.5K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.5K
One-Compartment Open Model for Extravascular Administration: First-Order Absorption Model01:15

One-Compartment Open Model for Extravascular Administration: First-Order Absorption Model

715
The first-order absorption model for extravascular administration describes the rate at which a drug is absorbed and eliminated, following the principles of first-order kinetics. This model is vital as it provides a mathematical representation of drug behavior within the body. It also allows for the prediction and interpretation of drug absorption and elimination based on the rate of change in drug concentration over time. This model can be visualized as a plasma concentration-time profile...
715

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interpretation drift in explainable AI under label noise.

Scientific reports·2026
Same author

Reply to the Letter to the Editor: Blind spots in radiology leadership regarding human resources and organization management in the era of artificial intelligence.

European radiology·2026
Same author

Leveraging a Quality and Safety Continuous Process Improvement Framework to Increase Breast Cancer Screening Access.

Journal of the American College of Radiology : JACR·2025
Same author

Leadership in radiology in the era of technological advancements and artificial intelligence.

European radiology·2025
Same author

Bridging the Gap: Evaluation of a Supplemental Surge Staffing Model to Maintain Radiology Turnaround Times Amid Labor Constraints.

Journal of the American College of Radiology : JACR·2025
Same author

Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.

Academic radiology·2025
Same journal

Comment on "Validation of a Single-Item Screener for Financial Toxicity in Outpatient Imaging Patients".

Journal of the American College of Radiology : JACR·2026
Same journal

Comparison of supplemental breast cancer screening outcomes for automated versus hand-held ultrasound.

Journal of the American College of Radiology : JACR·2026
Same journal

Screening Mammography Completion Among Women Enrolled in a Lung Cancer Screening Program.

Journal of the American College of Radiology : JACR·2026
Same journal

Hantavirus Infection Beyond the Lung: A Multi-Organ Radiological Perspective on Diagnosis, Imaging Modalities, and Precautionary Measures for Radiology Departments.

Journal of the American College of Radiology : JACR·2026
Same journal

ACR Appropriateness Criteria® Myelopathy: 2026 Update.

Journal of the American College of Radiology : JACR·2026
Same journal

ACR Appropriateness Criteria® Chronic Knee Pain: Update 2026.

Journal of the American College of Radiology : JACR·2026
See all related articles

Related Experiment Video

Updated: Apr 1, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.3K

Can We Predict Patient Wait Time?

Oleg S Pianykh1, Daniel I Rosenthal1

  • 1Department of Radiology, Harvard Medical School; Radiology Department, Massachusetts General Hospital, Boston, Massachusetts.

Journal of the American College of Radiology : JACR
|October 6, 2015
PubMed
Summary
This summary is machine-generated.

Predicting patient wait times is crucial for hospital workflow. This study identified optimal regression models based on wait-line sizes, offering accurate and efficient wait-time predictions for improved patient flow.

Keywords:
Hospital Information SystemQueueing Theorypredictive modelsregressiontime series

More Related Videos

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
14:56

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

Published on: January 27, 2010

22.2K
E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

9.4K

Related Experiment Videos

Last Updated: Apr 1, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.3K
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
14:56

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

Published on: January 27, 2010

22.2K
E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

9.4K

Area of Science:

  • Healthcare Management
  • Operations Research
  • Health Informatics

Background:

  • Patient wait times significantly impact hospital operations and patient satisfaction.
  • Effective management of patient queues is essential for optimizing clinical workflow.

Purpose of the Study:

  • To investigate the predictability of patient wait times.
  • To identify the key predictors influencing patient wait durations.

Main Methods:

  • A comprehensive list of 25 wait-related parameters was compiled from existing literature and experimental observations.
  • Parameters were selected for their derivability from standard Hospital Information System datasets.
  • Various time-predicting models were evaluated, with optimal parameter subsets identified through exhaustive model search and applied to actual patient wait data.

Main Results:

  • The study identified highly efficient wait-time prediction factors and models, notably line-size models.
  • These models demonstrated both high accuracy and computational efficiency.
  • The developed models were successfully implemented in clinical settings, displaying predicted wait times on monitors.

Conclusions:

  • Regression models utilizing wait-line sizes provide accurate and efficient patient wait-time predictions.
  • The findings support the integration of such models into hospital information systems for enhanced patient flow management.